HTTP API and External Systems for Health Management

Data Characteristics

Health management regulation data originates from internal medical institution rules, normative documents published by national health commissions, industry association guidelines, and health management service agreements. These documents are typically stored in formats like PDF, DOCX, and HTML. They are largely unstructured and contain extensive natural language descriptions. Update frequency is relatively low, usually quarterly or annually, but may include temporary updates for policy or regulatory changes. Data fields include, but are not limited to, regulation name, publication date, implementation details, applicable scope, responsible departments, oversight mechanisms, risk assessment standards, and emergency plans. Documents often contain medical terminology, legal clauses, and management process numbers. Units are frequently time-based (days, months, years), percentages (compliance rate, achievement rate), or levels (A, B, C).

Constraints Imposed by These Characteristics on "HTTP API and External Systems"

The unstructured nature of health management regulation documents requires HTTP APIs to have robust text parsing and content extraction capabilities during data ingestion. This allows for identifying key information from complex document structures. A low update frequency means data synchronization strategies can utilize periodic full or incremental updates, reducing real-time synchronization pressure. The specific medical terminology and legal clauses within documents demand higher accuracy from the model for understanding and question answering. This may necessitate customized glossaries or domain-specific model support. For processes and responsible departments mentioned in regulations, external system integration requires HTTP APIs to link Q&A results with internal business systems (e.g., OA systems, risk management systems). This can trigger approval workflows or responsibility notifications. The specific units in the data require API responses to maintain unit consistency, preventing confusion.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800-1200 characters`	Regulation documents have long logical paragraphs; this ensures semantic completeness. Shorter segments lead to severe fragmentation, while longer segments result in information overload.
`Overlap Length`	`100 characters`	Ensures contextual continuity between adjacent segments, improving recall accuracy, especially for content spanning pages or sections.
`Similarity threshold`	`0.7-0.75`	Regulation texts are highly specialized. A higher threshold reduces recall of irrelevant content; calibrate based on actual measurements.
`Recall count`	`8-12 entries`	Regulation Q&A often requires multi-perspective information support. Increasing recall covers more relevant details.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF files takes a long time; this provides sufficient time to prevent timeouts and parsing failures.
`maxContext`	`4096 tokens`	Regulation Q&A often requires a long context for reasoning, ensuring the model can handle complex logical relationships.

Common Pitfalls

HTTP API calls return HTTP 400 Bad Request, with logs showing invalid parameter: document_id. This may occur if the external system passes an invalid document identifier or if the identifier does not match FastGPT's internal storage.
After a user query, the answer contains only partial regulation clauses and lacks completeness. This is due to an unreasonable document segmentation strategy, leading to key information being fragmented or insufficient recall to cover the full context.
During external system integration, some fields (e.g., 责任部门, 审批流程) are not correctly mapped to internal business systems. This can happen if the HTTP API's returned data structure does not match the external system's expected structure, or if the field parser is incorrectly configured.

Verification Steps

Upload representative health management regulation documents. Verify successful parsing and basic Q&A functionality. Check if answers include key information points.
Simulate external system requests via HTTP API calls. Check if the returned JSON structure is complete and if field names and data types match expectations.
Ask questions about complex processes or multi-departmental collaboration scenarios within the regulations. Evaluate the model's logical rigor and information comprehensiveness. Confirm accurate citation of original regulation text.
Monitor system logs to confirm no abnormal errors during file parsing, vectorization, and Q&A response. Ensure response times are within an acceptable range.

The values provided are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.